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Critical Care

, 17:226 | Cite as

Clinical review: Can we predict which patients are at risk of complications following surgery?

  • Nirav Shah
  • Mark Hamilton
Review
Part of the following topical collections:
  1. Perioperative monitoring

Abstract

There are a vast number of operations carried out every year, with a small proportion of patients being at highest risk of mortality and morbidity. There has been considerable work to try and identify these high-risk patients. In this paper, we look in detail at the commonly used perioperative risk prediction models. Finally, we will be looking at the evolution and evidence for functional assessment and the National Surgical Quality Improvement Program (in the USA), both topical and exciting areas of perioperative prediction.

Keywords

Cardiopulmonary Exercise Testing Cardiac Risk Index Preoperative Risk Stratification Risk Prediction Score Risk Stratification Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

ACS

American College of Surgeons

APACHE

Acute Physiology and Chronic Health Evaluation

ASA

American Society of Anesthetists

MET

metabolic equivalent

NSQIP

National Surgical Quality Improvement Program

POSSUM

Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity.

Introduction

There are an estimated 234 million surgical operations every year worldwide [1], of which 4.2 million operations are carried out in England [2]. A precise estimation of perioperative complications and postoperative morbidity is difficult to gain, but it has been suggested this may occur in between 3 and 17% of cases [3, 4]. This wide range in reported complications is probably related to variable reporting, as well as disputed classification of complications. These complications cover a range of organ systems, including gastrointestinal, infectious, pulmonary, renal, haematological and cardiovascular [5, 6]. These complications can be anaesthetic related (for example, postoperative nausea and vomiting or hypoxaemia in the recovery room) or surgical (for example, wound related, ileus or haemorrhage).

Postoperative mortality across all procedures is approximately 0.5%, although it may exceed 12% in older patients undergoing emergency surgery in the UK [7]. A small high-risk group of patients has been shown to be responsible for approximately 83% of deaths and significantly longer hospital stays, despite making up only 12.5% of hospital admissions for surgery [7]. Of note, almost 90% of the patients in this high-risk group had emergency surgery, but <15% of them were admitted to critical care directly from the operating theatre. Comparatively, cardiac surgery in traditionally high-risk patients will routinely admit the majority of its patients to critical care postoperatively. Cardiac surgery has openly published mortality rates for a number of years. These rates have demonstrated a steady improvement, with a typical mortality rate of <2 to 3% [8].

Ideally, we would like to identify the patients who are most likely to suffer postoperative complications or mortality - both to inform the decision to operate, and to target postoperative care and critical care provision for these patients. Unfortunately, outcomes for patients undergoing surgery currently vary widely, and (particularly emergency) surgical care is often disjointed and may not be appropriately patient centred [9].

Complications

Accurate figures for surgical complication rates are difficult to obtain because of the lack of consensus amongst surgeons on what constitutes a postoperative complication. This difficulty is further exacerbated by disagreement on a structured classification of postoperative complications and morbidity, making it difficult to compare different surgical techniques or predictive models for surgical complications. In 1992 a model for classification of surgical complications was proposed by Clavien and colleagues [10]. Uptake of this model of classification was slow, due in part to a lack of evidence of international validation. The model was updated in 2004, and evaluated in a large cohort of patients by an international survey. This new model allows grading of postoperative complications, regardless of the initial surgery. The different categories are broad, permitting clear placement of complications in the various grades (Table 1).
Table 1

Classification of surgical complications

Grade

Description

1

Minor complication that can be easily treated on the ward with simple procedures or medications (for example, intravenous catheter, nasogastric tube, anti-emetic, simple analgesic)

2

Postoperative transfusion or treatment with medications other than those simple agents permitted under grade 1

3a

Needing invasive therapy - either surgical, endoscopic or radiological without general anaesthesia

3b

Needing invasive therapy - either surgical, endoscopic or radiological with general anaesthesia

4a

Single-organ dysfunction requiring high-dependency unit/ICU admission

4b

Multiorgan dysfunction requiring high-dependency unit/ICU admission

5

Death

Suffix 'd'

Added if the patient is suffering from a complication at discharge

Adapted from [11].

To accurately record postoperative complications, it is important to have a validated questionnaire. The Postoperative Morbidity Survey is one such questionnaire [5, 11]. This survey is well-validated and provides objective evidence of postoperative complications, fitting the classification described above, and has been validated in a UK population [6] (Tables 2 and 3).
Table 2

Clinical examples of postoperative complications

Grade

Organ system

Example

1

Cardiac

Arrhythmia cardioconverting with electrolytes

 

Respiratory

Fluid overload requiring diuretics

 

Neurological

Mild delirium, self-limiting

 

Gastrointestinal

Drug-related diarrhoea

 

Renal

Mild acute renal failure (not requiring treatment)

2

Cardiac

Atrial fibrillation requiring ß-blockade/digoxin

 

Respiratory

Pneumonia needing antibiotics and/or oxygen

 

Neurological

Transient ischaemic attack

 

Gastrointestinal

Ileus needing nasogastric/further treatment

 

Renal

Urinary tract infection needing antibiotics

3a

Cardiac

Bradyarrhythmia needing pacing wire

 

Respiratory

Effusion needing chest drain

 

Neurological

Extra/subdural haematoma needing evacuation

 

Gastrointestinal

Pseudo-obstruction needing flatus tube

 

Renal

 

3b

Cardiac

Tachyarrhythmia needing direct current cardioversion

 

Respiratory

Bronchopleural fistula post thoracic surgery

 

Neurological

Extra/subdural haematoma needing evacuation

 

Gastrointestinal

Anastomic leakage needing surgery

 

Renal

Stenosis of ureters after transplantation

4a

Cardiac

Heart failure requiring ionotropes

 

Respiratory

Pneumonia needing intubation

 

Neurological

Cerebrovascular accident/haemorrhage

 

Gastrointestinal

Pancreatitis

 

Renal

Acute renal failure

4b

 

Any combination of the above

Adapted from [11].

Table 3

The Postoperative Morbidity Survey

Morbidity type

Criterion

Source of data

Respiratory

Postoperative need for oxygen or respiratory support

Patient observation, drug chart

Microbiology

Antibiotics or pyrexia >38°C in previous 24 hours

Observation chart, drug chart

Renal

Oliguria, raised serum creatinine, new urinary catheter

Fluid balance chart, biochemistry result, patient observation

Gastrointestinal

Failure of enteral feeding

Patient questioning, fluid balance chart, drug chart

Cardiovascular

Diagnostic tests or treatment within last 24 hours for any of:

Drug chart, note review

 

new myocardial infarct or ischaemia, hypotension, arrhythmias,

 
 

cardiogenic pulmonary oedema, thrombotic event

 

Neurological

Cerebrovascular accident/transient ischaemic attack, confusion,

Note review, patient questioning

 

delirium, coma

 

Haematological

Use within last 24 hours of: packed red cells, platelets, fresh-frozen

Drug chart, fluid balance chart

 

plasma, cryoprecipitate

 

Surgical wound

Wound dehiscence/infection needing exploration or drainage of pus

Note review, pathology result

Pain

New pain requiring parenteral opioids or regional analgesia/anaesthesia

Drug chart, patient questioning

Adapted from [6].

Guidelines

There are a number of guidelines available to both aid in the identification of and guide the care of the high-risk patient.

In 2010 the Association of Anaesthetists of Great Britain and Ireland published guidelines on the preoperative assessment of a patient having an anaesthetic [12]. This document encourages a formal preoperative assessment process, which should start the process of identifying high-risk patients, as well as preparing the patient for their anaesthetic. These guidelines incorporate the guidelines issued by the National Institute for Clinical Excellence in 2003 on the use of routine preoperative tests for elective surgery [13].

The American Heart Association published guidelines on perioperative cardiovascular evaluation and care for noncardiac surgery in 2007 [14]. These were updated in 2009 to incorporate new evidence relating to perioperative β-blockade [15]. Similar guidelines were also issued by the European Society of Cardiology and endorsed by the European Society of Anesthesiology in 2009 [16]. One important predictive element suggested by the guidelines is the use of metabolic equivalents (METs): 1 MET is the oxygen consumption of a 40-year-old, 70 kg man, and is approximately 3.5 ml/minute/kg. Patients unable to reach 4 METS (equivalent of climbing a flight of stairs) are suggested to be at increased risk during surgery [17].

The Royal College of Surgeons of England and the Department of Health have also set up a Working Group on the Perioperative Care of the Higher Risk General Surgical Patient, which has issued a set of guidelines on the care of the high-risk surgical patient [9]. In addition to the detection of complications following surgery, these guidelines emphasise the importance of a rapid, appropriate response to limit the number and severity of complications. Part of this response would include appropriate early use of critical care facilities.

Risk prediction

Evidently it would be preferable to identify high-risk patients prior to starting any operations. To make this identification it is necessary to have an agreed definition of what constitutes a high-risk patient. The Royal College of Surgeons of England Working Group has defined a high-risk patient as one with an estimated mortality ≥5%, with consultant presence being encouraged if this value exceeds 10%. The group go on to suggest that any patients with estimated mortality >10% should be admitted to critical care postoperatively.

To accurately estimate probable mortality and morbidity, we should ideally use an approach that combines the patient's physiological characteristics with the procedure to be carried out to calculate a predictive risk. The ideal risk prediction score should be simple, easily reproducible, objective, applicable to all patients and operations, and both sensitive and specific. Furthermore, this score should be equally easily applied to both the emergent and non-emergent patient and setting. Whilst in the non-emergent setting the anaesthetist has access to all of the patients' investigations and to more elaborate physiological investigations, the emergent scenario requires decisions based on the acute physiological condition and quick investigations. The two scenarios can therefore be very different, and it may not be possible to use one risk score for both emergent and non-emergent operations.

There are various risk scoring systems that have been described in the literature. These systems can be classified as those estimating population risk or individual risk [18, 19]. Scores predicting individual risk can be general, organ specific, or procedure specific. It is important not to use population-based scoring systems in isolation to make individual decisions because they cannot always be extrapolated to specific patients.

An example of a general score that is based on estimating population risk is the American Society of Anesthetists (ASA) classification [20]. The ASA classification was not originally composed as a risk prediction score, although it is often used as such. The different ASA classes have been shown to be good predictors of mortality [21], while the rate of postoperative morbidity has also been noted to vary with class [22]. The ASA system has the advantage of being a simple, easily applied score, which is widely known. However, the ASA classification is subjective and does not provide individual or procedure specific information. The system has also been shown to have poor sensitivity and specificity for individual patient morbidity and mortality [23].

The Charlson Comorbidity Index is a generic score based on weighting various preoperative diseases and predicting long-term survival [24]. This score is relatively simple to use, but also does not take into account the surgical operation, and relies on a subjective assessment of the patient, which may lead to errors. As such, it tends to be used as a research tool rather than in daily clinical practice [25].

In 1999 Lee and colleagues published a Revised Cardiac Risk Index [26]. This index is a scoring system used solely to predict the risk of major cardiac events after non-cardiac surgery. Whilst the Revised Cardiac Risk Index is a simple, well-validated system that also considers the scale of surgery undertaken, it can only be used to predict single-organ risk.

The Acute Physiology and Chronic Health Evaluation (APACHE) score was first introduced in 1981 [27] before the updated APACHE II score was published in 1985 [28]. The APACHE II system assigns a score based on 12 physiological variables, with further points for age and chronic health, but it does not consider the type of surgery undertaken as the score was originally designed for use in critical care. This score therefore provides an individualised risk of mortality and morbidity, but does not differentiate between different procedures. Despite this lack of differentiation, APACHE has been shown to give a better prediction of outcome than the ASA system [29], and has been shown to predict different levels of surgical complications (minor, major and death). APACHE III and APACHE IV have subsequently been released, but have not been validated to the same extent as APACHE II for preoperative risk prediction. In addition, these scores are considerably more complex, requiring 17 physiological variables to be measured over the first 24 hours of critical care stay. This requirement for the variables to be recorded over the first 24 hours of critical care stay is present in all variations of the APACHE score, and is a major impediment to the regular use of this score preoperatively in emergency or urgent surgery.

A derivation of the APACHE system that is useful for comparing patients with different diseases is the Simplified Acute Physiology Score II [30]. This score also requires the collection of 17 variables over the first 24 hours of critical care stay, resulting in a predicted mortality score. The Simplified Acute Physiology Score II is not designed for use in perioperative prediction, although it can be used in this field.

The Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (POSSUM) score was designed for use in preoperative risk prediction, allowing for both individual physiological risk and the type of surgery performed [31]. This scoring system examines 12 physiological and six operative variables, which are then entered into two mathematical equations to predict mortality and morbidity. Unfortunately, there was a tendency to overpredict mortality in low-risk patients as a result of using logistic regression to predict risk (the lowest possible mortality risk is 1.08%). In 1998 Portsmouth-POSSUM was published in an attempt to reduce this overprediction [32]. Whilst improving the mortality scoring, Portsmouth-POSSUM did not update the equation for morbidity scoring. Another variation of POSSUM is colorectal-POSSUM, designed in 2004 for use in colorectal surgery [33]. Despite some evidence that POSSUM may overestimate or underestimate risk in specific populations, POSSUM and its various surgery-specific iterations remain the most validated and used scoring system for predicting individual patient risk (Table 4).
Table 4

Comparison of risk prediction scoring systems

Risk prediction system

Description

Advantages

Disadvantages

American Society of Anesthetists

Numerical scale (1 to 5) based on severity of co-morbidities

Simple, easily applied, well known

Subjective, not individual or procedure specific, poor sensitivity and specificity

Charlson Comorbidity Score

Additive score based on weighting of preoperative diseases

Simple, better predictor than American Society of Anesthetists, good at estimating population risk

Subjective, does not look at procedure, mainly used as a research tool

Revised Cardiac Risk Index

Scoring system based on presence of one of six major co-morbidities and the severity of operation

Simple, well validated and good for predicting cardiac risk

Single-organ risk, broad categories, assessment of severity of operation is subjective

Acute Physiology and Chronic Health Evaluation

12 to 17 variables, measured over 24 hours

Individualised predictor of risk of mortality and morbidity, better predictor of outcome than American Society of Anesthetists, well known

Multiple variables over 24 hours of critical care, can be difficult to score before emergent surgery, not designed for use perioperatively

Simplified Acute Physiology Score

17 variables measured over 24 hours

Well validated for predictive mortality

Multiple variables over 24 hours of critical care, can be difficult to score before emergent surgery, not designed for use perioperatively

Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity

Scoring of 12 physiological and six operative variables, which are then entered into two mathematical equations to predict mortality and morbidity

Best validated and known/used scores for perioperative prediction various surgery-specific variations for specific areas

May overestimate or underestimate mortality and morbidity in specific populations due to use of logarithmic regression

These scores are often used to calculate the mortality and morbidity risk prior to surgery. However, it is important to keep in mind the fact that high-risk surgery may still be of benefit in certain patients. It is also important not to base postoperative critical care admissions purely on the scoring systems above. To this end, strict admission and discharge criteria from and to a critical care unit remain difficult to objectivise. Occasionally we will see patients who do not have a high score on the above systems, but clinically are frail, have multiple minor co-morbidities, or have fewer more significant co-morbidities. Treating these cases as high-risk patients with postoperative critical care is important despite the low score. Ultimately, the various risk stratification scores can only be accurate for a proportion of patients, and there will always be patients in whom they are not accurate. These patients are those who can only be selected out through clinical acumen, or by paying attention to the much-talked-about gut feeling.

Important to remember is that some scores are designed to be calculated preoperatively (POSSUM), while others are designed for postoperative use (APACHE). While the scores can be adapted and used at any stage in the patient's care, they may not be as accurate.

An area of anaesthetic preoperative assessment that is receiving a high level of interest currently is functional assessment. Traditionally, functional assessment has always been a part of preoperative assessment prior to the removal of organs (pulmonary testing before pneumonectomy or dimercaptosuccinic acid scan before nephrectomy). In addition, functional testing is often used to quantify the level of disease in a patient with known disease (stress echocardiography or pulmonary function testing).

Cardiopulmonary exercise testing is an integrated test that looks at both cardiac and pulmonary function. This testing involves incremental physical exercise, up to the patient's maximal level (at which they are unable to do more, or become symptomatic). Whilst doing this exercise, the ventilatory effort, inspiratory and expiratory gasses, blood pressure and electrocardiogram are recorded. These are used to calculate two values - the body's maximal oxygen uptake and the point at which anaerobic metabolism exceeds aerobic metabolism (anaerobic threshold). These figures are used to demonstrate the ability of the cardiopulmonary system to oxygenate the body. Measurement of the maximal oxygen uptake, and hence the patient's true MET status, by cardiopulmonary exercise testing has demonstrated that the traditional estimation of MET is often inaccurate. This inaccuracy has led to increased identification of patients that have increased risk without being symptomatic or having identifiable factors in their medical and anaesthetic history. Cardiopulmonary exercise testing has long been shown to have good predictive value for postoperative complications in pulmonary resection surgery [34, 35].

There is now increasing evidence for the benefit of using cardiopulmonary exercise testing in general surgery as a predictive test for postoperative morbidity and mortality [36, 37, 38, 39, 40]. However, there are still doubts about the evidence base in certain surgical specialties and hence the global suitability of cardiopulmonary exercise testing at present [41].

In 1991, in the USA, the National Veterans Affairs Surgical Risk Study prospectively collected data on major operations at 44 Veterans Affairs hospitals [42]. Based on these data, the study developed risk-adjusted models for 30-day morbidity and mortality for a number of surgical subspecialties [43, 44]. Following on from this study, the Veterans Affairs National Surgical Quality Improvement Program (NSQIP) was set up in 1994 at all of the Veterans Affairs hospitals, leading to a 45% reduction in morbidity and a 27% decrease in mortality (and hence large cost savings) [45]. The NSQIP was subsequently expanded to include a number of university teaching hospitals in the Patient Safety in Surgery study funded by the American College of Surgeons (ACS) from 2001 to 2004. The Patient Safety in Surgery study demonstrated a significantly lower 30-day unadjusted mortality for men in the study hospital [46, 47].

As a result, in 2004 the ACS-NSQIP was started. By 2008, 198 hospitals were receiving ACS-NSQIP feedback on their outcomes [48]. Using the hospitals with lower morbidity or mortality as benchmarks to identify the adjustable factors in poor outcomes in individual hospitals, these factors can be changed to improve outcomes [49, 50]. One example of this relates to colectomies performed in ACS-NSQIP enrolled hospitals. These operations have been shown to increasingly be performed laparoscopically in these hospitals, with significant reductions in most major complications (including surgical-site infections, pneumonia and sepsis) [51]. One should remember despite the potential benefits of the ACS-NSQIP programme that there are limits to its usefulness. The input of data is labour intensive, and the results are only as good as the data input. Furthermore, the results are based on interpretation of data in specific categories, thus missing complications that do not fall into these specific areas [52, 53, 54]. This ACS-NSQIP programme is also building up a large database of information that should hopefully produce more effective risk stratification scores in the future.

One area of healthcare policy that is very topical is the improved outcomes provided by carrying out certain operations in fewer high-volume surgical centres [55, 56]. Low-risk patients, however, have been shown to have comparable outcomes in both low-volume and high-volume centres [57]. The moderate-risk to high-risk patients do still have better outcomes in the larger regional centres. Hence, it is important to risk stratify a patient before selecting a hospital for an elective operation (the local smaller hospital may still be an appropriate place to undergo surgery).

Conclusion

Currently, preoperative risk stratification is often not part of the standard preoperative assessment (with the exception of the ASA classification). There are a number of reasons for this omission. The currently available scores are often complicated, needing multiple tests or time to complete. Facilities and staff time/training may not be available for functional testing. Traditionally, junior doctors, in addition to their other clinical duties, carried out preoperative assessment - they may not have been aware of the guidelines and risk stratification scores for use in surgery. Additionally, mortality and morbidity tables for individual hospitals and surgeons/surgeries are not routinely published for noncardiac surgery. As a result, this is often not a priority for hospital managers or clinicians who may or may not know accurate outcome statistics for their patients. However, the current financial restraints on the National Health Service are likely to lead to renewed efforts to reduce the length of stay in hospital by reducing postoperative morbidity. The government's stated aim to increase competition (and in so doing improve results) is likely to lead to increased interest in also reducing mortality. In the absence of a British version of NSQIP, there is likely to be increased focus on preoperative risk stratification scoring. As well as potentially reducing costs and improving performance, preoperative scoring has the potential to ensure better informed consent and patient/procedural selection, as well as appropriate targeting of postoperative critical care services.

Unfortunately, all of the currently used risk scoring systems have limitations. These limitations include inter-observer variability for the ASA classification, the complicated nature and need for 24 hours of observations with APACHE, and the overestimation of mortality in lower risk groups with POSSUM. The single-organ scores are often useful in predicting organ dysfunction, but only provide a limited picture. The present limitations do not preclude the use of the tests, but ensure that it is important to select the test based on the patient population and the surgery being performed. Currently assigning patients to bands of risk (that is, high, medium or low) may be the best we can achieve, but it is still not a routine calculation.

An area of great interest in preoperative assessment for elective surgery is functional testing. This area presently generates a lot of debate, with strong views on both sides. There is good evidence for the use of functional testing in specific surgical specialties. However, the situation does remain unclear in other forms of surgery. In addition, functional testing is time consuming, and requires investment and training to get started. This investment is clearly difficult at present with budgets being reduced across the board. To become established, further evidence is needed to demonstrate its relevance across all surgical specialties. This is an area that is still in its infancy, but as further research is carried out will probably become more established and see wider use. The potential to provide individualised risk prediction based on an individual's physiological response to stress is an exciting area, with the possibility of high predictive value and better use of critical resources to improve patient care.

Note

This is part of a series on Perioperative monitoring, edited by Dr Andrew Rhodes

Notes

Supplementary material

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Authors’ original file for figure 4

References

  1. 1.
    Weiser TG, Regenbogen SE, Thompson KD, Haynes AB, Lipsitz SR, Berry WR, Gawande AA: An estimation of the global volume of surgery: a modelling strategy based on available data. Lancet 2008, 372: 139-144. 10.1016/S0140-6736(08)60878-8CrossRefPubMedGoogle Scholar
  2. 2.
    Hospital Episode Data[http://www.hesonline.nhs.uk]
  3. 3.
    Kable AK, Gibberd RW, Spigelman AD: Adverse events in surgical patients in Australia. Int J Qual Health Care 2002, 14: 269-276. 10.1093/intqhc/14.4.269CrossRefPubMedGoogle Scholar
  4. 4.
    Gawande AA, Thomas EJ, Zinner MJ, Brennan TA: The incidence and nature of surgical adverse events in Colorado and Utah in 1992. Surgery 1999, 126: 66-75. 10.1067/msy.1999.98664CrossRefPubMedGoogle Scholar
  5. 5.
    Bennett-Guerrero E, Welsby I, Dunn TJ, Young LR, Wahl TA, Diers TL, Phillips-Bute BG, Newman MF, Mythen MG: The use of a postoperative morbidity survey to evaluate patients with prolonged hospitalization after routine, moderate-risk, elective surgery. Anesth Analg 1999, 89: 514-519.PubMedGoogle Scholar
  6. 6.
    Grocott MPW, Browne JP, Van der Meulen J, Matejowsky C, Mutch M, Hamilton MA, Levett DZH, Emberton M, Haddad FS, Mythen MG: The Postoperative Morbidity Survey was validated and used to describe morbidity after major surgery. J Clin Epidemiol 2007, 60: 919-928. 10.1016/j.jclinepi.2006.12.003CrossRefPubMedGoogle Scholar
  7. 7.
    Pearse RM, Harrison DA, James P, Watson D, Hinds C, Rhodes A, Grounds RM, Bennettt ED: Identification and characterisation of the high-risk surgical population in the United Kingdom. Crit Care 2006, 10: R81. 10.1186/cc4928PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Survival Rates - Heart Surgery in United Kingdom[http://heartsurgery.cqc.org.uk/Survival.aspx]
  9. 9.
    Anderson I, Eddleston J, Grocott M, Lees N, Lobo D, Loftus I, Markham N, Mitchell D, Pearse R, Peden C, Sayers R, Wigfull J: The Higher Risk General Surgical Patient: Towards Improved Care for a Forgotten Group. London: Royal College of Surgeons of England, Department of Health; 2011.Google Scholar
  10. 10.
    Clavien P, Sanabria J, Strasberg S: Proposed classification of complication of surgery with examples of utility in cholecystectomy. Surgery 1992, 111: 518-526.PubMedGoogle Scholar
  11. 11.
    Dindo D, Demartines N, Clavien PA: Classification of surgical complications. Ann Surg 2004, 240: 205-213. 10.1097/01.sla.0000133083.54934.aePubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Verma R, Wee MYK, Hartle A, Alladi V, Rollin AM, Meakin G, Struthers R, Carlisle J, Johnston P, Ricett K, Hurley C: Pre-operative Assessment and Patient Preparation. London: Association of Anaesthetists of Great Britain and Ireland; 2010.Google Scholar
  13. 13.
    National Institute for Clinical Excellence: Pre-operative Tests, the Use of Routine Pre-operative Tests for Elective Surgery. London: NICE; 2003.Google Scholar
  14. 14.
    Fleisher LA, Beckman JA, Brown KA, Calkins H, Chaikof EL, Fleischmann KE, Freeman WK, Froehlich JB, Kasper EK, Kersten JR, Riegel B, Robb JF, Smith SC Jr, Jacobs AK, Adams CD, Anderson JL, Antman EM, Buller CE, Creager MA, Ettinger SM, Faxon DP, Fuster V, Halperin JL, Hiratzka LF, Hunt SA, Lytle BW, Nishimura R, Ornato JP, Page RL, Riegel B, et al.: ACC/AHA 2007 guidelines on perioperative cardiovascular evaluation and care for noncardiac surgery: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Revise the 2002 Guidelines on Perioperative Cardiovascular Evaluation for Noncardiac Surgery). J Am Coll Cardiol 2007, 50: e159-e241. 10.1016/j.jacc.2007.09.003CrossRefPubMedGoogle Scholar
  15. 15.
    Fleisher LA, Beckman JA, Brown KA, Calkins H, Chaikof EL, Fleischmann KE, Freeman WK, Froehlich JB, Kasper EK, Kersten JR, Riegel B, Robb JF, American Society of Echocardiography; American Society of Nuclear Cardiology; Heart Rhythm Society; Society of Cardiovascular Anesthesiologists; Society for Cardiovascular Angiography and Interventions; Society for Vascular Medicine; Society for Vascular Surge: 2009 ACCF/AHA focused update on perioperative beta blockade incorporated into the ACC/AHA 2007 guidelines on perioperative cardiovascular evaluation and care for noncardiac surgery. J Am Coll Cardiol 2009, 54: e13-e118. 10.1016/j.jacc.2009.07.010CrossRefPubMedGoogle Scholar
  16. 16.
    Poldermans D, Bax JJ, Boersma E, De Hert S, Eeckhout E, Fowkes G, Gorenek B, Hennerici MG, Iung B, Kelm M, Kjeldsen KP, Kristensen SD, Lopez-Sendon J, Pelosi P, Philippe F, Pierard L, Ponikowski P, Schmid JP, Sellevold OF, Sicari R, Van den Berghe G, Vermassen : Guidelines for preoperative cardiac risk assessment and perioperative cardiac management in noncardiac surgery: the Task Force for Preoperative Cardiac Risk Assessment and Perioperative Cardiac Management in noncardiac Surgery of the European Society of Cardiology (ESC) and endorsed by the European Society of Anaesthesiology (ESA). Eur Heart J 2009, 30: 2769-2812.CrossRefPubMedGoogle Scholar
  17. 17.
    Reilly DF, McNeely MJ, Doerner D, Greenberg DL, Staiger TO, Geist MJ, Vedovatti PA, Coffey JE, Mora MW, Johnson TR, Guray ED, Van Norman GA, Fihn SD: Self-reported exercise tolerance and the risk of serious perioperative complications. Arch Intern Med 1999, 159: 2185-2192. 10.1001/archinte.159.18.2185CrossRefPubMedGoogle Scholar
  18. 18.
    Barnett S, Moonesinghe S: Clinical risk scores to guide peioperative management. Postgrad Med J 2011, 87: 535-541. 10.1136/pgmj.2010.107169CrossRefPubMedGoogle Scholar
  19. 19.
    Rix T, Bates T: Pre-operative risk scores for the prediction of outcome in elderly people who require emergency surgery. World J Emerg Surg 2007, 2: 16. 10.1186/1749-7922-2-16PubMedCentralCrossRefPubMedGoogle Scholar
  20. 20.
    Saklad M: Grading of patients for surgical procedures. Anesthesiology 1941, 2: 281-284. 10.1097/00000542-194105000-00004CrossRefGoogle Scholar
  21. 21.
    Wolters U, Wolf T, Stutzer H, Schroder T: ASA classification and perioperative variables as predictors of postoperative out-come. Br J Anaesth 1996, 77: 217-222. 10.1093/bja/77.2.217CrossRefPubMedGoogle Scholar
  22. 22.
    Wolters U, Wolf T, Stutzer H, Schroder T, Pichlmaier H: Risk factors, complications, and outcome in surgery: a multivariate analysis. Eur J Surg 1997, 163: 563-568.PubMedGoogle Scholar
  23. 23.
    Akoh JA, Mathew AM, Chalmers JW, Finlayson A, Auld GD: Audit of major gastrointestinal surgery in patients aged 80 years or over. J R Coll Surg Edinb 1994, 39: 208-213.PubMedGoogle Scholar
  24. 24.
    Charlson ME, Pompei P, Ales KL, MacKenzie CR: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987, 40: 373-383. 10.1016/0021-9681(87)90171-8CrossRefPubMedGoogle Scholar
  25. 25.
    Moonesinghe SR, Mythen MG, Grocott MP: High-risk surgery: epidemiology and outcomes. Anesth Analg 2011, 112: 891-901. 10.1213/ANE.0b013e3181e1655bCrossRefPubMedGoogle Scholar
  26. 26.
    Lee TH, Marcantonio ER, Mangione CM, Thomas EJ, Polanczyk CA, Cook EF, Sugarbaker DJ, Donaldson MC, Poss R, Ho KK, Ludwig LE, Pedan A, Goldman L: Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation 1999, 100: 1043-1049. 10.1161/01.CIR.100.10.1043CrossRefPubMedGoogle Scholar
  27. 27.
    Knaus WA, Zimmerman JE, Wagner DP, Draper EA, Lawrence DE: APACHE-Acute Physiology and Chronic Health Evaluation: a physiologically based classification system. Crit Care Med 1981, 9: 591-597. 10.1097/00003246-198108000-00008CrossRefPubMedGoogle Scholar
  28. 28.
    Knaus WA, Draper EA, Wagner DP, Zimmerman JE: APACHE II: a severity of disease classification system. Crit Care Med 1985, 13: 818-829. 10.1097/00003246-198510000-00009CrossRefPubMedGoogle Scholar
  29. 29.
    Goffi L, Saba V, Ghiselli R, Necozione S, Mattei A, Carle F: Preoperative APACHE II and ASA scores in patients having major general surgical operations: prognostic value and potential clinical applications. Eur J Surg 1999, 165: 730-735. 10.1080/11024159950189483CrossRefPubMedGoogle Scholar
  30. 30.
    Le Gall J-R, Lemeshow S, Saulnier F: A New Simplified Acute Physiology Score (SAPS II) based on a European/North American Multicenter Study. JAMA 1993, 270: 2957-2963. 10.1001/jama.1993.03510240069035CrossRefPubMedGoogle Scholar
  31. 31.
    Copeland GP, Jones D, Walters M: POSSUM: a scoring system for surgical audit. Br J Surg 1991, 78: 355-360. 10.1002/bjs.1800780327CrossRefPubMedGoogle Scholar
  32. 32.
    Prytherch DR, Whiteley MS, Higgins B, Weaver PC, Prout WG, Powell SJ: POSSUM and Portsmouth POSSUM for predicting mortality. Physiological and Operative Severity Score for the enUmeration of mortality and morbidity. Br J Surg 1998, 85: 1217-1220. 10.1046/j.1365-2168.1998.00840.xCrossRefPubMedGoogle Scholar
  33. 33.
    Tekkis PP, Prytherch DR, Kocher HM, Senapati A, Poloniecki JD, Stamatakis JD, Windsor AC: Development of a dedicated risk-adjustment scoring system for colorectal surgery (colorectal POSSUM). Br J Surg 2004, 91: 1174-1182. 10.1002/bjs.4430CrossRefPubMedGoogle Scholar
  34. 34.
    Bayram AS, Candan T, Gebitekin C: Preoperative maximal exercise oxygen consumption test predicts postoperative pulmonary morbidity following major lung resection. Respirology 2007, 12: 505-510. 10.1111/j.1440-1843.2007.01097.xCrossRefPubMedGoogle Scholar
  35. 35.
    Bolliger CT, Jordan P, Soler M, Stulz P, Gradel E, Skarvan K, Elsasser S, Gonon M, Wyser C, Tamm M, et al.: Exercise capacity as a predictor of postoperative complications in lung resection candidates. Am J Respir Crit Care Med 1995, 151: 1472-1480. 10.1164/ajrccm.151.5.7735602CrossRefPubMedGoogle Scholar
  36. 36.
    Older P, Hall A, Hader R: Cardiopulmonary exercise testing as a screening test for perioperative management of major surgery in the elderly. Chest 1999, 116: 355-362. 10.1378/chest.116.2.355CrossRefPubMedGoogle Scholar
  37. 37.
    Carlisle J, Swart M: Mid-term survival after abdominal aortic aneurysm surgery predicted by cardiopulmonary exercise testing. Br J Surg 2007, 94: 966-969. 10.1002/bjs.5734CrossRefPubMedGoogle Scholar
  38. 38.
    Epstein SK, Freeman RB, Khayat A, Unterborn JN, Pratt DS, Kaplan MM: Aerobic capacity is associated with 100-day outcome after hepatic transplantation. Liver Transpl 2004, 10: 418-424. 10.1002/lt.20088CrossRefPubMedGoogle Scholar
  39. 39.
    Nagamatsu Y, Yamana H, Fujita H, Hiraki H, Matsuo T, Mitsuoka M, Hayashi A, Kakegawa T: The simultaneous evaluation of preoperative cardiopulmonary functions of oesophageal cancer patients in the analysis of expired gas with exercise testing. Nippon Kyobu Geka Gakkai Zasshi 1994, 42: 2037-2040.PubMedGoogle Scholar
  40. 40.
    Smith TB, Stonell C, Purkayastha S, Paraskevas P: Cardiopulmonary exercise testing as a risk assessment method in non cardio-pulmonary surgery: a systematic review. Anaesthesia 2009, 64: 883-893. 10.1111/j.1365-2044.2009.05983.xCrossRefPubMedGoogle Scholar
  41. 41.
    Young EL, Karthikesalingam A, Huddart S, Pearse RM, Hinchliffe RJ, Loftus IM, Thompson MM, Holt PJ: A systematic review of the role of cardiopulmonary exercise testing in vascular surgery. Eur J Vasc Endovasc Surg 2012, 44: 64-71. 10.1016/j.ejvs.2012.03.022CrossRefPubMedGoogle Scholar
  42. 42.
    Khuri SF, Daley J, Henderson WG, Barbour G, Lowry P, Irvin G: The National Veterans Administration Surgical Risk Study: risk adjustment for the comparative assessment of the quality of surgical care. J Am Coll Surg 1995, 180: 519-531.PubMedGoogle Scholar
  43. 43.
    Khuri SF, Daley J, Henderson WG, Hur K, Gibbs JO, Barbour G: Risk adjustment of the postoperative mortality rate for the comparative assessment of the quality of surgical care. J Am Coll Surg 1997, 185: 315-327.PubMedGoogle Scholar
  44. 44.
    Daley J, Khuri SF, Henderson WG, Hur K, Gibbs JO, Barbour G: Risk adjustment of the postoperative morbidity rate for the comparative assessment of the quality of surgical care. J Am Coll Surg 1997, 185: 328-340.PubMedGoogle Scholar
  45. 45.
    Khuri SF, Daley J, Henderson WG, Hur K, Hur K, Demakis J, Aust JB: The Department of Veterans Affairs' NSQIP. The first national, validated, outcome-based, risk-adjusted, and peer-controlled programme for the measurement and enhancement of the quality of surgical care. Ann Surg 1998, 228: 491-507. 10.1097/00000658-199810000-00006PubMedCentralCrossRefPubMedGoogle Scholar
  46. 46.
    Khuri SF, Henderson WG, Daley J, Jonasson O, Jones RS, Campbell DA Jr: The Patient Safety in Surgery Study: background, study design, and patient populations. J Am Coll Surg 2007, 204: 1089-1102. 10.1016/j.jamcollsurg.2007.03.028CrossRefPubMedGoogle Scholar
  47. 47.
    Henderson WG, Khuri SF, Mosca C, Fink AS, Hutler MM, Neumayer LA: Comparison of risk-adjusted 30-day postoperative mortality and morbidity in Department of Veterans Affairs hospitals and selected university medical centres: general surgical operations in men. J Am Coll Surg 2007, 204: 1103-1114. 10.1016/j.jamcollsurg.2007.02.068CrossRefPubMedGoogle Scholar
  48. 48.
    Birkmeyer JD, Shahian DM, Dimick JB, Finlayson SR, Flum DR, Ko CY, Hall BL: Blueprint for a new American College of Surgeons National Surgical Quality Improvement Program. J Am Coll Surg 2008, 207: 777-782. 10.1016/j.jamcollsurg.2008.07.018CrossRefPubMedGoogle Scholar
  49. 49.
    Pitt HA, Kilbane M, Strasberg SM, Pawlik TM, Dixon E, Zyromski NJ, Aloia TA, Henderson JM, Mulvihill SJ: ACS-NSQIP has the potential to create an HPBNSQIP option. HPB 2009, 11: 405-413. 10.1111/j.1477-2574.2009.00074.xPubMedCentralCrossRefPubMedGoogle Scholar
  50. 50.
    Khuri SF: The NSQIP: a new frontier in surgery. Surgery 2005, 138: 837-843. 10.1016/j.surg.2005.08.016CrossRefPubMedGoogle Scholar
  51. 51.
    Ozhathil DK, Li Y, Smith JK, Witkowski E, Coyne ER, Alavi K, Tseng JF, Shah SA: Colectomy performance improvement within NSQIP 2005-2008. J Surg Res 2011, 171: e9-e13. 10.1016/j.jss.2011.06.052CrossRefPubMedGoogle Scholar
  52. 52.
    Cima RR, Lackore KA, Nehring SA, Cassivi SD, Donohue JH, Deschamps C, Vansuch M, Naessens JM: How best to measure surgical quality? Comparison of the Agency for Healthcare Research and Quality Patient Safety Indicators (AHRQ-PSI) and the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) postoperative adverse events at a single institution. Surgery 2011, 150: 943-949. 10.1016/j.surg.2011.06.020CrossRefPubMedGoogle Scholar
  53. 53.
    Yu P, Chang DC, Osen HB, Talamini MA: NSQIP reveals significant incidence of death following discharge. J Surg Res 2011, 170: e217-e224. 10.1016/j.jss.2011.05.040CrossRefPubMedGoogle Scholar
  54. 54.
    Lee LC, Reines HD, Sheridan MJ, Farmer BE, Martin J, Duan M: Apples and oranges: comparison of ACS-NSQIP observed outcomes with premier's quality manager-predicted outcomes. Am J Med Qual 2011, 26: 474-479. 10.1177/1062860611401652CrossRefPubMedGoogle Scholar
  55. 55.
    Halm EA, Lee C, Chassin MR: Is volume related to outcome in health care? A systematic review and methodologic critique of the literature. Ann Intern Med 2002, 137: 511-520. 10.7326/0003-4819-137-6-200209170-00012CrossRefPubMedGoogle Scholar
  56. 56.
    Bentrem DJ, Brennan MF: Outcomes in oncologic surgery: does volume make a difference? World J Surg 2005, 29: 1210-1216. 10.1007/s00268-005-7991-xCrossRefPubMedGoogle Scholar
  57. 57.
    Bilimoria KY, Bentrem DJ, Talamonti MS, Stewart AK, Winchester DP, Ko CY: Risk-based selective referral for cancer surgery. Ann Surg 2010, 251: 708-716. 10.1097/SLA.0b013e3181c1bea2CrossRefPubMedGoogle Scholar

Copyright information

© BioMed Central Ltd 2013

Authors and Affiliations

  1. 1.General Intensive Care UnitSt George's HospitalLondonUK

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